Application of de novo sequencing to large-scale complex proteomics datasets

ABSTRACT: Dependent on concise, pre-defined protein sequence databases, traditional search algorithms perform poorly when analyzing mass spectra derived from wholly uncharacterized protein products. Conversely, de novo peptide sequencing algorithms can interpret mass spectra without relying on reference databases. However, such algorithms have been difficult to apply to complex protein mixtures, in part due to a lack of methods for automatically validating de novo sequencing results. Here, we present novel metrics for benchmarking de novo sequencing algorithm performance on large scale proteomics datasets, and present a method for accurately calibrating false discovery rates on de novo results. We also present a novel algorithm (LADS) which leverages experimentally disambiguated fragmentation spectra to boost sequencing accuracy and sensitivity. LADS improves sequencing accuracy on longer peptides relative to other algorithms and improves discriminability of correct and incorrect sequences. Using these advancements, we demonstrate accurate de novo identification of peptide sequences not identifiable using database search-based approaches.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Bos Taurus (bovine) Saccharomyces Cerevisiae (baker's Yeast)

TISSUE(S): Brain

SUBMITTER: Arun Devabhaktuni  

LAB HEAD: Joshua E. Elias

PROVIDER: PXD003317 | Pride | 2016-01-12

REPOSITORIES: Pride